Netzebenen übergreifende Modellkopplungskonzepte für die Energiesystemanalyse

Eine Vielzahl von Fragestellungen in der Energiesystemanalyse wird mittels der Kopplung von Modellen adressiert. Die zu Grunde liegenden Kopplungskonzepte entstehen dabei oft intuitiv oder getrieben durch das Vorhandensein bestimmter Modelle. Die Entscheidung für eine bestimmte Art der Modellkopplung impliziert allerdings eine Reihe von Vor- und Nachteilen hinsichtlich Aspekten wie i) der Reproduzierbarkeit, ii) des Weiterentwicklungspotentials oder der iii) Nutzbarkeit zum Beantworten verschiedener inhaltlicher Fragestellungen. Ziel des durchgeführten Workshops war die Erarbeitung einer Übersicht und Einordnung von Modellkopplungskonzepten in laufenden Forschungsvorhaben und ein Erfahrungsaustausch mit den Workshop-Teilnehmern hinsichtlich der Punkte i), ii) und iii). Thematischer Schwerpunkt war dabei die Netzebenen übergreifende Modellierung von zukünftigen Energiesystemen

High Performance Computing vs. Heuristic: A performance benchmark for optimization problems with linear power flows

We address the crucial aspect of unmanageable computing times of large-scale Energy System Optimization Models. Such models provide insights into future energy supply systems while keeping an overall perspective. However, the degree of phenomena or processes to be modeled is ever-increasing. With PIPS-IPM++ a novel solver is presented which is designed for linear optimization problems with linking variables (i.e. investment decisions) and linking constraints (i.e. power flow constraints). Compared to existing approaches for computing time reduction it makes use of High Performance Computing. Here, we present a benchmark study that compares the performance of PIPS-IPM++ with a usual speed-up heuristic (Temporal Zooming). Our results show that speed-ups between factor 10 and 15 are achievable with PIPS-IPM++ and Temporal Zooming, respectively. Despite PIPS-IPM++ has a great potential to parallelize solving, the tested version of the solver is especially useful for models without investment decisions (i.e. optimal power flow problems)

Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems

Solar energy is envisaged as a major pillar of the global transition to a climate-friendly energy system. Variability of solar radiation requires additional balancing measures to ensure a stable and secure energy supply. In order to analyze this issue in detail, solar radiation time series data of appropriate temporal and spatial resolution is necessary. Common weather models and satellites are only delivering solar surface irradiance with temporal resolutions of up to 15 min. Significant short-term variability in irradiances within seconds to minutes however is induced by clouds. Ground-based measurements typically used to capture this variability are costly and only sparsely available. Hence, a method to synthetically generate time series from currently available satellite imagery is of value for researchers, grid operators, and project developers. There are efforts to increase satellite resolution to 1 min, but this is not planned everywhere and will not change the spatial resolution. Therefore, the fundamental question remains if there are alternative strategies to obtain high temporal resolution observations at a pinpoint. This paper presents a method to generate 1 min resolved synthetic time series of global and direct normal irradiances for arbitrary locations. A neural network based on satellite image derived cloud structure parameters enables to classify high-frequency solar radiation variability. Combined with clear-sky radiation data, 1 min time series which reflect the typical variability characteristics of a site are reproduced. Testing and validation against ground observations (BSRN) show that the method can accurately reproduce characteristics such as frequency and ramp distributions. An application case demonstrates the usage in low-voltage grid studies

ATTRAQT'EM, 2024-2026, DLR-VE: Anwendungen, Schnittstellen und Datenformate für Algorithmen des Quantencomputing in der Energiesystem-Modellierung

Eine Vielzahl von Herausforderungen im Kontext der Energiewende lässt sich durch Energiesystemmodellierung (ESM) adressieren. Für die Betriebs- und Investitionsentscheidungen werden hierbei Strom-, Gas- und Wärmeversorgung in hoher zeitlicher und räumlicher Auflösung simuliert bzw. optimiert. Das Lösen groß-skaliger Optimierungsprobleme (OP) mit klassischer Hardware und Solver-Software stößt in der ESM zunehmend an Grenzen. Ein vollaufgelöstes OP des Hochspannungsnetzes Deutschlands (Sektorkopplung inklusive) lässt sich beispielsweise mit herkömmlichen Mitteln nicht mehr lösen. Aus diesem Grund stellt die Entwicklung von Quantenalgorithmen für OP in der ESM eine große Chance dar

Pushing computational boundaries: Solving integrated investment planning problems for large-scale energy systems with PIPS-IPM+

Energy policies for setting the course of future energy supply often rely on models of energy systems with increasing interdependencies. On the mathematical side this translates into linking variables and constraints in the structure of optimization problems. Challenges concerning limited computing resources are often tackled from the applied side since generic parallel solvers are not available. This means that modelers today aim to simplify real-world models when implementing new features, despite of lots of effort spent for improving them before. This prevents accurately modeling of all system components. We tackle this challenge by combining both domain knowledge from the application side and the solver side and demonstrate our solution for a real-world model which is practically not solvable with existing methods. Therefore, we parameterize instances of the energy system optimization model REMix having more than 700 Mio. non-zeros. For the first time, these model instances incorporate both the optimization of a full hourly operational time horizon and path-dependent long-term investment planning for the German power system. These instances are annotated in a way, that the corresponding linear problems (LPs) decompose into blocks of similar size. To solve the annotated LPs, the new interior-point solver PIPS-IPM++ is applied. It treats large numbers of linking variables and constraints using a hierarchical algorithm and enables efficient scaling on parallel hardware. In this sense, we expand the boundaries of what is computationally possible when solving LPs in energy systems analysis. Accordingly, using the best possible real-world models becomes practicable, which enables the calibration of simplified models in a domain where validation is difficult

UNSEEN: Bewertung der Unsicherheiten in linear optimierenden Energiesystemmodellen unter Zuhilfenahme Neuronaler Netze

Der Einsatz von Modellen zur Erstellung und Untersuchung von Szenarien ist ein wesentliches Instrument der Energiesystemanalyse. Für die Politikberatung ist die Frage nach der Verlässlichkeit von solchen Szenarien von großer Wichtigkeit, da diese mit großen Unsicherheiten behaftet sein können. Diesem Problem wird in UNSEEN begegnet: durch das Abfahren eines sehr großen Parameterraums sollen weit mehr als 1000 Energieszenarien automatisch generiert, berechnet und ausgewertet werden. Hierzu zählen insbesondere auch Extremszenarien. Eine wesentliche Herausforderung ist dabei die Senkung von Modellrechenzeiten zur Lösung gemischt-ganzzahliger Optimierungsprobleme. Im Vorläuferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open Source Solvers PIPS-IPM++ die Voraussetzung für den Einsatz von Hochleistungscomputern zur performanten Lösung dieser Modelle gelegt. Die grundlegende Idee für die Weiterentwicklung ist es eine Methode des Maschinellen Lernens (Reinforcement Learning) zu verwenden, um schnelle Vorhersagen der Ergebnisse eines Optimierungsproblems zu erhalten und diese als Startlösung für einen deterministischen Lösungsalgorithmus zu nutzen. Mittels Modellkopplungen und statistischer Analysen werden ex-post ausführliche Auswertungen des entstehenden Szenarioraums durchgeführt. Hierzu werden multi-kriterielle Indikatoren (u. a. zu Angemessenheit, Betriebssicherheit und Wirtschaftlichkeit) von möglichen, zukünftigen Stromversorgungssystemen ermittelt. Auf dieser Grundlage sollen abschließend Methoden entwickelt werden, um besonders interessante Punkte innerhalb des Szenarioraums gezielt ansteuern können

Carbon-neutral power system enabled e-kerosene production in Brazil in 2050

Rich in renewable resources, extensive acreage, and bioenergy expertise, Brazil, however, has no established strategies for sustainable aviation fuels, particularly e‑kerosene. We extend the lens from the often‑studied economic feasibility of individual e‑kerosene supply chains to a system‑wide perspective. Employing energy system analyses, we examine the integration of e‑kerosene production into Brazil’s national energy supplies. We introduce PyPSA‑Brazil, an open‑source energy system optimisation model grounded in public data. This model integrates e‑kerosene production and offers granular spatial resolution, enabling federal‑level informed decisions on infrastructure locations and enhancing transparency in Brazilian energy supply scenarios. Our findings indicate that incorporating e‑kerosene production can bolster system efficiency as Brazil targets a carbon‑neutral electricity supply by 2050. The share of e‑kerosene in meeting kerosene demand fluctuates between 2.7 and 51.1%, with production costs varying from 113.3 to 227.3 €/MWh. These costs are influenced by factors such as biokerosene costs, carbon pricing, and export aspirations. Our findings are relevant for Brazilian policymakers championing aviation sustainability and offer a framework for other countries envisioning carbon‑neutral e‑kerosene production and export

Evaluation of uncertainties in linear energy system optimization models using HPC and neural networks

Within the interdisciplinary BMWK-funded project UNSEEN, experts from High Performance Computing, mathematical optimization and energy systems analysis combine strengths to evaluate uncertainties in modeling and planning future energy systems with the aid of High Performance Computing (HPC) and neural networks. Energy System Models (ESM) are central instruments for realizing the energy transition. These models try to optimize complex energy systems in order to ensure security of supply while minimizing costs for power production and transmission. In order to derive reliable and robust policy advice for decision makers, hundreds or even thousands of ESM problems need to be solved in order to address uncertainties in a given model and dataset.Mixed-integer linear programs (MIPs), a direct extension of Linear programs (LPs), can be used to formulate and compute more concrete and realistic energy systems. Since the availability of fast LP solvers is a major prerequisite for optimizing MIPs, the development of an open-source scalable distributed-memory LP solver, called PIPS-IPM++, was started in a preceding project and can already outperform state-of-the-art solvers. A second prerequisite for efficient MIP solving is the availability of MIP heuristics. For this purpose, we develop a generic MIP framework including reinforcement learning methods. Moreover, we aim to implement an efficient automated HPC workflow for generating, solving, and postprocessing numerous ESM problems with a special structure in order to develop new tools for better predictions about the future of our energy system. This novel approach couples multiple existing and new software packages to achieve the project goals

Teaching Power-Sector Models Social and Political Awareness

Energy-system scenarios are widely used to relate the developments of the energy supply and the resulting carbon-emission pathways to political measures. To enable scenario analyses that adequately capture the variability of renewable-energy resources, a specialised type of power-sector model (PSM) has been developed since the beginning of this century, which uses input data with hourly resolution at the national or subnational levels. These models focus on techno-economic-system optimisation, which needs to be complemented with expert socioeconomic knowledge in order to prevent solutions that may be socially inacceptable or that oppose political goals. A way to integrate such knowledge into energy-system analysis is to use information from framework scenarios with a suitable geographical and technological focus. We propose a novel methodology to link framework scenarios to a PSM by applying complexity-management methods that enable a flexible choice of base scenarios that are tailored to suit different research questions. We explain the methodology, and we illustrate it in a case study that analyses the influence of the socioeconomic development on the European power-system transition until 2050 by linking the power-sector model, REMix (renewable-energy mix), to regional framework scenarios. The suggested approach proves suitable for this purpose, and it enables a clearer link between the impact of political measures and the power-system development